Characterizing shape, appearance and motion is an important issue in medical imaging applications. One approach to accomplish this characterization is the use of active models. Active shape models (ASMs) depict shape statistics using principal component analysis. Active appearance models (AAMs) extend the ASM to model the appearance so that both shape and appearance are jointly modeled using principal component analysis. The ASM and AAM are applicable to individual images only.
To deal with a video, active appearance motion models (AAMM) extend the AAM to characterize the motion in the video and is used for segmenting a spatiotemporal object. One restriction of the AAMM is that no global motion is allowed before neighboring frames; hence the AAMM is not applicable to online tracking.
For most visual tracking applications, measurement data are uncertain and sometimes missing: images are taken with noise and distortion, while occlusions can render part of the object-of-interest unobservable. Uncertainty can be globally uniform; but in most real-world scenarios, it is heteroscedastic in nature, i.e., both anisotropic and inhomogeneous. A good example is the echocardiogram (ultrasound heart data). Ultrasound is prone to reflection artifacts, e.g., specular reflectors, such as those that come from membranes. Because of the single “view direction”, the perpendicular surface of a specular structure produces strong echoes, but tilted or “off-axis” surfaces may produce weak echoes, or no echoes at all (acoustic “drop out”). For an echocardiogram, the drop-out can occur at the area of the heart where the tissue surface is parallel to the ultrasound beam. In addition, left ventricle appearance changes are caused by fast movement of the heart muscle, respiratory inferences, unnecessary transducer movement, etc.
Due to its availability, relative low cost, and noninvasiveness, cardiac ultrasound images are widely used for assessing cardiac functions. In particular, the analysis of ventricle motion is an efficient way to evaluate the degree of ischemia and infarction. Segmentation or detection of the endocardium wall is the first step towards quantification of elasticity and contractility of the left ventricle. There is a need for a method for improved shape tracking of an object, such as a left ventricle.